本文選題：落地銑鏜床　切入點：綜合誤差建模　出處：《哈爾濱工業(yè)大學》2015年博士論文　論文類型：學位論文

【摘要】：重型數(shù)控落地銑鏜床具有開放式結構,適合大型零部件高精度平面和孔系的加工,但是精度不足限制了該機床的應用。針對該機床空間誤差進行控制和補償有著重要的現(xiàn)實意義和研究價值。然而對該類機床的精度檢測和誤差補償技術的研究還不夠完善。本文首先對重型數(shù)控落地銑鏜床進行了誤差溯源分析,在提出了該機床的幾何誤差和熱誤差的耦合方式的基礎上,建立了包含幾何與熱耦合誤差和動態(tài)跟蹤誤差的綜合誤差模型,并推導了機床誤差敏感系數(shù)矩陣。分析誤差測量不確定度并提出測量精度保持方法。依據(jù)統(tǒng)計方法得到了激光跟蹤儀測量要素的不確定度分布。通過實驗研究得出了機床低溫升時以熱漂移誤差為主要熱誤差形態(tài)的特性,建立了空間測量點熱漂移誤差模型,測量得出了機床重復定位誤差的分布,綜合跟蹤儀和機床對測量不確定度的影響,依據(jù)Monte Carlo法評估了測量不確定度。為降低測量不確定度,優(yōu)化了跟蹤儀的測量站位,同時提出了工作空間分割和多站位測量方法,通過實驗驗證了上述方法對降低測量不確定度的有效性。在分區(qū)測量空間誤差的基礎上辨識幾何誤差。依據(jù)誤差敏感系數(shù)矩陣分析得到了空間誤差對幾何誤差的敏感性以及機床空間尺寸(Y軸、W軸、Z軸坐標和刀具尺寸)對敏感性的影響規(guī)律。在此基礎上提出了采用激光跟蹤儀的四線制測量與粒子群優(yōu)化算法相結合的幾何誤差辨識方法。該方法引入了粒子群優(yōu)化算法,辨識得到了垂直度誤差、線性位移誤差和轉角誤差。以激光干涉儀的測量結果為依據(jù)對比驗證了上述誤差辨識方法的有效性。為辨識動態(tài)跟蹤誤差建立了機床的跟隨誤差和圓軌跡輪廓誤差模型。測量了機床X軸和Y軸聯(lián)動的圓軌跡誤差,分析得到圓軌跡誤差隨機床運動參數(shù)的變化規(guī)律,基于此變化規(guī)律研究了動態(tài)跟蹤誤差和準靜態(tài)誤差的解耦方法。結合圓軌跡輪廓誤差模型和解耦得到的動態(tài)跟蹤誤差,辨識得到了機床運動軸的參數(shù),通過試驗驗證了預測模型精度�；诒孀R得出的綜合誤差模型參數(shù)建立了誤差模型,驗證了綜合誤差模型預測精度,并著重開展了基于有限元方法(ANASYS軟件)和實驗方法的機床Z向空間誤差溯源分析,建立了主軸箱體結構熱變形的誤差尺寸鏈,闡述了Z向熱漂移誤差的產(chǎn)生原因。針對熱漂移誤差設計開發(fā)了基于因瓦合金桿的熱誤差實時測量與補償系統(tǒng)。本文采用自主開發(fā)的適用于西門子數(shù)控系統(tǒng)的誤差補償器,補償機床靜態(tài)空間誤差,實驗驗證了補償?shù)挠行�。依�?jù)辨識得到的伺服參數(shù),調(diào)整系統(tǒng)動態(tài)適配時間,機床圓軌跡誤差降低了80%。開展了熱漂移誤差補償裝置在西門子數(shù)控系統(tǒng)中的應用方法研究,補償前后的對比結果驗證了補償系統(tǒng)的穩(wěn)定有效性。最終,將誤差補償技術綜合應用到重型數(shù)控落地銑鏜床中,補償效果顯著,加工誤差降低35%以上。
[Abstract]:The heavy-duty NC floor milling and boring machine has an open structure, which is suitable for the machining of large parts with high precision plane and hole system. However, the lack of precision limits the application of the machine tool. It has important practical significance and research value to control and compensate the spatial error of the machine tool. However, the research on the accuracy detection and error compensation technology of this kind of machine tool is also made. In this paper, the error of heavy NC ground milling and boring machine is analyzed. On the basis of the coupling mode of geometric error and thermal error of the machine tool, a comprehensive error model including geometric and thermal coupling error and dynamic tracking error is established. The error sensitivity coefficient matrix of machine tool is derived, the uncertainty of error measurement is analyzed, and the measurement precision preserving method is put forward. The uncertainty distribution of measuring elements of laser tracker is obtained by statistical method. The characteristic that the thermal drift error is the main thermal error form when the machine tool rises at low temperature is given. The thermal drift error model of spatial measurement points is established, and the distribution of repeated positioning errors of machine tools is measured. The influence of comprehensive tracker and machine tool on measurement uncertainty is obtained. The measurement uncertainty is evaluated according to Monte Carlo method in order to reduce the uncertainty of measurement. The measuring station of tracker is optimized, and the method of workspace segmentation and multi-station measurement is put forward. The effectiveness of the above method in reducing the uncertainty of measurement is verified by experiments. The geometric error is identified on the basis of the spatial error of the zone measurement, and the geometric error of the spatial error is obtained by the analysis of the error sensitivity coefficient matrix. The sensitivity, the space dimension of machine tool, the coordinate of Z axis of Y axis and the size of cutting tool) affect the sensitivity. On the basis of this, the geometry combining four-wire measurement with laser tracker and particle swarm optimization algorithm is put forward. This method introduces particle swarm optimization (PSO) algorithm. The verticality error is identified. Based on the measurement results of laser interferometer, the validity of the above error identification method is verified. In order to identify the dynamic tracking error, the tracking error and the circular trajectory error of the machine tool are established. Differential model. The circular trajectory errors of X-axis and Y-axis linkage of machine tools have been measured. The variation law of circular trajectory error with machine tool motion parameters is obtained. Based on the variation law, the decoupling method of dynamic tracking error and quasi-static error is studied, and the dynamic tracking error obtained by decoupling is combined with the circular trajectory contour error model and decoupling. The parameters of the moving axis of the machine tool are identified, and the accuracy of the prediction model is verified by experiments. The error model is established based on the parameters of the comprehensive error model obtained from the identification, and the prediction accuracy of the comprehensive error model is verified. Based on finite element method ANASYS software) and experimental method, the error dimension chain of the thermal deformation of spindle box structure is established, which is based on the analysis of the Z-direction spatial error of the machine tool, which is based on the finite element method (FEM) and the experimental method. This paper describes the causes of the Z-direction thermal drift error, designs and develops a real-time measurement and compensation system for the thermal error based on the Intile alloy rod, and adopts an error compensator developed by ourselves for Siemens numerical control system. The validity of the compensation is verified by experiments. According to the identified servo parameters, the dynamic adaptation time of the system is adjusted. The error of circular trajectory of machine tool is reduced by 80%. The application method of thermal drift error compensation device in Siemens numerical control system is studied. The comparison results before and after compensation verify the stability and effectiveness of the compensation system. The error compensation technology is applied to the heavy NC ground milling and boring machine. The compensation effect is remarkable and the machining error is reduced by more than 35%.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TG659
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本文編號：1574620